The many ROI benefits of predictive marketing analytics are fueling adoption of the technology. That said, frequently the messaging from vendors is confusing, overlapping and sometimes forward thinking versus what capabilities are available today.

Set your organization up for success by focusing on your unique B2B marketing needs or specific use cases to ensure that the predictive marketing analytics solution you choose will solve your specific business problems. Ask the tough internal questions about your organization’s specific needs and the specific use cases that are driving the purchase. And, ask the PMA vendors about challenges that B2B Marketers face when implementing predictive marketing analytics solutions.

How do you provide automated and accurate access to integrate data from various sources (from both on-premises and in the cloud) and different types (text, transactional data, streams, linked data), in order to transform and prepare that data for modeling?

Predictive Marketing Analytics Buying Checklist – Intent

Beware of solutions that require a manual process to extend data to social and digital media platforms.

Look out for behavioral signals that are limited to nurture scores from the customer’s marketing systems or intent scores from a third party.

Seek out high-quality demographic and behavioral attributes at the contact level.

Make sure the ability exists to leverage third-party intent signals in modeling.

Determine intent not only at the account level but also at the contact level.

Remember that insightful sales intelligence and contextual relevance needs to be layered on top of intent to facilitate meaningful and relevant sales conversations.

It’s key to not only identify prospects with a high propensity to buy (fit) but to also evaluate intent (a high probability that a company is actively looking).

Predictive Marketing Analytics Buying Checklist – Modeling

It’s important to have a library of pre-built models that are best in class that can be modified.

How do you empower end-users to create accurate models at the end-user level?

Beware of purchasing a black box solution and not understanding the signals behind its’ scores and predictions.

How are insights provided to B2B marketers in an intuitive manner to help then interpret the significance of indicators so they can recommend actions?

Dependency on MRP for model building, inspection, and management can affect prediction quality.

Machine-learning algorithms and processes that are sophisticated and technically intricate need to be examined and understood.

How does your solution model to find look-alike accounts with similar attributes should be out of the box?

How does your solution define the right segments on which to model – hyper-segment customers or prospects?

Automatic and continuously updating algorithms that are based in data science and machine learning should be a checkbox item.

The ability to visually interact with and explore the data, and perform basic descriptive statistics and pattern detection should be standard.

Predictive Marketing Analytics Buying Checklist – Self-Service UX

Does the solution offer an intuitive UI suited for demand gen and ABM marketers to facilitate flawless operational execution is a basic requirement?

It’s important to have automation that eliminates the need to outsource core sales and marketing functions.

Self-service means that training, the need for an administrator or customizations performed by the vendor are not required.

Predictive Marketing Analytics Buying Checklist – Prerequisites

Have a go to market strategy in place that is mutually agreed upon by B2B sales and marketers.

Have a mature marketing team (one that is beyond scoring) that can take advantage of the more advanced business-level outcomes.

Ensure strong analytics and data science practices are in place, understood, are updated with intelligence.

Your organization is able to incorporate fully automated marketing operations that are outside of less-tech-savvy marketers’ comfort zones.

In sum, there are many factors to consider when making a predictive marketing analytics purchase. Start today with the Predictive Marketing Analytics Buying Checklist. And freely add, delete or modify it to ensure that your organization makes an informed, objective and fact-based purchase decision.

About Peter

Peter is a strategic and visionary marketing executive and brand champion who has leveraged his unique combination of classical training and entrepreneurial experience at start-ups and F500 companies to transform technology innovations into multimillion-dollar revenue streams. His experience spans all areas of marketing, including go-to-market strategy and execution; brand identity and brand positioning; product development; sales and marketing leadership; customer acquisition and retention; and influencer and analyst relations. Peter consults with c-level executives, teaches at USF’s EMBA program and serves as an advisor to start-ups.

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About After 25 years of Marketing with F500 and startups, Peter formed Four Quadrant, LLC in 2004 so he could work with as many CEOs, GMs, VPS and VPMs as possible. The focus has always been to provide more experience than what an organization thought they needed to ensure that considerable value was delivered with each engagement, with the shortest ramp and the most efficient spend.